Energy Systems Research



I enjoy connecting the components of complex systems in novel ways that benefit society. This drives my fascination with urban energy systems and my goal to improve global energy sustainability. I pursue that goal by combining engineering and operations research methods to explore problems at the intersection of cities, energy, economics, and policy.

My current work uses system modeling techniques to design autonomous urban energy systems that consume electricity more flexibly in support of a smarter, cleaner electric grid. This flexible electricity consumption allows millions of autonomous devices to participate in a dynamic electricity market to do things like balance solar and wind output. My current research program studies 1) how household energy consumption can be electrified, stored, and automated; 2) how that automation can be coordinated at the neighborhood scale, and 3) how those neighborhood aggregations can interact with electricity markets to reduce power sector emissions.

Postdoctoral Researcher
Engineering & Public Policy
Carnegie Mellon University
Azevedo Research Group

Ph.D., Mechanical Engineering
The University of Texas at Austin
Webber Energy Group

current projects

Flexible residential heating and cooling for integrating renewables and reducing emissions

This project studies how residential space conditioning can be electrified and integrated with smart thermostats, building thermal mass storage, and active thermal energy storage. These systems can be controlled to shift electricity demand timing to reduce electric grid emissions and balance intermittent renewable generation. The high-renewables, low-emissions electric grids of the future will benefit from these types of flexible electricity demand.

Quantifying the power sector value of large-scale residential energy efficiency measures

Building energy efficiency investment aims to lower energy consumption in ways that reduce cost, peak electricity demand, and emissions. Since electric grid cost, demand, and emissions are constantly changing, the value of energy efficiency investment cannot be fully quantified without estimating its impact on electricity demand time series. This study develops a method for estimating how large-scale energy efficiency investment affects hourly residential electricity demand profiles and integrates those results with an electric grid model to quantify the cost, capacity, and emissions benefits of large-scale building energy efficiency programs.

Simulating marginal emissions factors for the U. S. power sector

Marginal emissions factors (MEFs) estimate the change in power sector emissions due to a change in electricity demand. This estimation is useful for approximating the emissions impacts of research projects looking at solar development, electric vehicle charging, and other activities. MEFs are traditionally calculated from historical data, which limits their application for studying future scenarios. This project develops a method to calculate MEFs using simulated data, which allows us to estimate the MEFs for future power sector scenarios.